Since the 1970s, acid rain has remained in the public spotlight in both Europe and the United States and recently has emerged as an important problem in other regions such as Southeast Asia. To reveal responses of Masson Pine to acid rain during a long time series in central China, we used the interpolation dataset of acid rain and the Global Inventory Modeling and Mapping Studies (GIMMS) norma...

A vegetation cover increase has been identified at global scales using satellite images and vegetation indices. This fact is usually explained by global climatic change processes such as CO(2) and temperature increases. Nevertheless, although these causes can be important, the role of socioeconomic transformations must be considered in some places, since in several areas of Northern Hemisphere ...

Within the context of climate change, it is of utmost importance to quantify the stability of ecosystems with respect to climate anomalies. It is well acknowledged that ecosystem stability may change over time. As these temporal stability changes may provide a warning for increased vulnerability of the system, this study provides a methodology to quantify and assess these temporal changes in ve...

Upscaling ecological information to larger scales in space and downscaling remote sensing observations or model simulations to finer scales remain grand challenges in Earth system science. Downscaling often involves inferring subgrid information from coarse-scale data, and such ill-posed problems are classically addressed using regularization. Here, we apply two-dimensional Tikhonov Regularizat...

This paper evaluated the capacity of SPOT VEGETATION time-series to monitor herbaceous fuel moisture content (FMC) in order to improve fire risk assessment in the savanna ecosystem of Kruger National Park in South Africa. In situ herbaceous FMC data were used to assess the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Vegetation Dryness Index (VDI), Im...

The Normalized Di erence Vegetation Index (NDVI) is widely accepted as a good indicator for providing vegetation properties and associated changes for large scale geographic regions. Using multivariate time series data analysis methods based on Principal Component transform and Wavelet Decomposition, a sequence of 11-year monthly Advanced Very High Resolution Radiometer (AVHRR)-derived NDVI dat...

Global change affects vegetation cover and processes through multiple pathways. Long 1 time series of surface land surface properties derived from satellite remote sensing give unique 2 abilities to observe these changes, particularly in areas with complex topography and limited research 3 infrastructure. Here, we focus on Nepal, a biodiversity hotspot where vegetation productivity is 4 limited...

Crop condition assessment in the early growing stage is essential for crop monitoring and crop yield prediction. A normalized difference vegetation index (NDVI)-based method is employed to evaluate crop condition by inter-annual comparisons of both spatial variability (using NDVI images) and seasonal dynamics (based on crop condition profiles). Since this type of method will generate false info...

Detailed information from global remote sensing has greatly advanced our understanding of Earth as a system in general and of agricultural processes in particular. Vegetation monitoring with global remote sensing systems over long time periods is critical to gain a better understanding of processes related to agricultural change over long time periods. This specifically relates to sub-humid to ...

The objectives of the study are to integrate the conditional Latin Hypercube Sampling (cLHS), sequential Gaussian simulation (SGS) and spatial analysis in remotely sensed images, to monitor the effects of large chronological disturbances on spatial characteristics of landscape changes including spatial heterogeneity and variability. The multiple NDVI images demonstrate that spatial patterns of ...